用相似性信号自动选最优模型合并方式，省时省力
SimMerge: Learning to Select Merge Operators from Similarity Signals
- 基于模型间相似性信号预测合并效果，无需反复试错
- 在70亿参数模型上超越固定合并方法，1110亿参数也有效
- 支持在线新增任务和合并算子，适合大规模模型管理
模型合并能将多个模型融合为一个具备综合能力的模型，是大语言模型开发的重要工具。但规模化合并面临挑战：性能依赖于合并算子、模型子集和合并顺序，常需代价高昂的试错评估。本文提出SimMerge，一种利用低成本、任务无关的模型间相似性信号预测高绩效合并的方法。仅需少量无标签探针，即可提取功能与结构特征，预测候选两两合并的表现，实现合并算子、顺序与模型子集的高效选择，无需迭代评估。实验表明，SimMerge在70亿参数模型上持续优于最佳固定合并算子，并可泛化至多路合并及1110亿参数模型，无需重新训练。我们还引入带赌博机制的变体，支持在线添加新任务与算子。结果表明，学习如何合并可实现大规模模型组合的可扩展性，尤其适用于检查点数量庞大且评估预算有限的场景。
原文摘要 · Abstract (English)
Model merging combines multiple models into a single model with aggregated capabilities, making it a powerful tool for large language model (LLM) development. However, scaling model merging is challenging: performance depends on the choice of merge operator, model subset, and merge order, often requiring expensive merge-and-evaluate searches. In this work, we introduce SimMerge, a predictive merge-selection method that identifies high-performing merges using inexpensive, task-agnostic similarity signals between models. Given a small set of unlabeled probes, SimMerge extracts functional and structural features to predict the performance of candidate two-way merges, enabling merge operator, order and model subset selection without iterative evaluation. We show that SimMerge consistently outperforms the best fixed merge operator across 7B-parameter LLMs and generalizes to multi-way merges and 111B-parameter LLMs without retraining. We further introduce a bandit variant that supports adding new tasks and operators online. Our results suggest that learning how to merge enables scalable model composition when checkpoint catalogs are large and evaluation budgets are limited.
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